Create face_recognition.py

face_recognition.py

@@ -0,0 +1,67 @@
+import numpy as np
+from PIL import Image
+import tensorflow as tf # Or import your PyTorch modules
+
+# --- Load your trained HTCNN model ---
+try:
+    htcnn_model = tf.keras.models.load_model('htcnn_model.h5') # Replace with your model path
+except Exception as e:
+    print(f"Error loading HTCNN model: {e}")
+    htcnn_model = None
+
+def extract_embedding(face_image):
+    """
+    Extracts the feature embedding from a face image using the HTCNN model.
+
+    Args:
+        face_image (numpy.ndarray): The cropped face image.
+
+    Returns:
+        numpy.ndarray: The feature embedding, or None if an error occurs.
+    """
+    if htcnn_model is None:
+        print("HTCNN model not loaded. Cannot extract embedding.")
+        return None
+
+    # Preprocess the face image (resize, normalize, etc.) as required by your model
+    resized_face = cv2.resize(face_image, (160, 160)) # Example size, adjust as needed
+    normalized_face = resized_face / 255.0 # Example normalization
+
+    # Ensure the input has the correct batch dimension
+    embedding = htcnn_model.predict(np.expand_dims(normalized_face, axis=0))[0]
+    return embedding
+
+def recognize_face(face_image, face_embeddings_db, threshold=0.6):
+    """
+    Recognizes a face by comparing its embedding with the embeddings in the database.
+
+    Args:
+        face_image (numpy.ndarray): The cropped face image.
+        face_embeddings_db (dict): Dictionary of known face embeddings (name: embedding).
+        threshold (float): The similarity threshold for recognition.
+
+    Returns:
+        str or None: The name of the recognized person, or None if no match is found.
+    """
+    embedding = extract_embedding(face_image)
+    if embedding is None:
+        return None
+
+    min_distance = float('inf')
+    recognized_identity = None
+
+    for name, stored_embedding in face_embeddings_db.items():
+        # Calculate the distance (e.g., Euclidean distance) between the embeddings
+        distance = np.linalg.norm(embedding - stored_embedding)
+        if distance < min_distance:
+            min_distance = distance
+            recognized_identity = name
+
+    if min_distance < threshold:
+        return recognized_identity
+    else:
+        return None
+
+if __name__ == '__main__':
+    # Example usage (requires having a trained HTCNN model and a database)
+    pass